Midterm Project: Numeric Processor
💡 This is an individual project. You are expected to work independently.
If you get stuck, confused, or have trouble with the project, you should use a channel in Discord, or message an instructor.
Numeric Processor
For the midterm project, you will write a program that processes numbers (reading through a data file and performing computation).
Your program will send requests to an API to assist with some of the computation.
This project has a few similarities with the microprocessor simulation project in Programming 1. This time, though, you will be using classes and methods, instead of functions.
Carefully read the README.md file in the project repository for full details of the tasks to complete.
Remember..
- Read the instructions
- Plan before you code
- Debug if you aren't getting the desired output
- Attend office hours if you need additional support
- Ask for help in Discord
Grading
| Criteria | Proficient | Competent | Developing |
|---|---|---|---|
| Coding Style (20%) | |||
| 1. Indentation and Formatting | Code is consistently well-indented and follows PEP 8 formatting guidelines. | Code is mostly well-indented and follows PEP 8 guidelines with minor deviations. | Code lacks consistent indentation and does not follow PEP 8 guidelines. |
| 2. Naming Conventions | Meaningful and consistent variable/function/class names following PEP 8 conventions. | Mostly meaningful names, with occasional inconsistencies. | Variable/function/class names are unclear or inconsistent. |
| 3. Comments and Documentation | Comprehensive comments and clear documentation for major functions and complex logic. | Adequate comments explaining major sections of code. | Lack of comments or insufficient documentation. |
| 4. Appropriate Use of Language Constructs | Demonstrates advanced understanding and appropriate use of Python language constructs (e.g., list comprehensions, generators). | Generally applies language constructs correctly, with occasional lapses. | Misuses or misunderstands key language constructs. |
| Persistence (50%) | |||
| 5. Completeness | Evidence that all components of the assignment were attempted. All functionality present. | Evidence that most elements of the assignment were attempted. Most functionality present. | Little evidence of completion of work. Incomplete or major functionality missing. |
| 6. Timeliness | Assignment started early (based on GitHub data). GitHub commits show steady progress. Submitted on time. | Assignment is submitted late but GitHub data demonstrates an early or reasonable start date, with significant iteration on arrival to solution (i.e., multiple commits showing progress) | Submitted late. GitHub repository data shows late start and minimal iteration. |
| 7. Use of Resources | Assignment is fully complete and provides all functionality. If assignment is not fully complete, student attended office hours (or additional help sessions) and/or asked high quality and timely questions on Discord. | Assignment is not fully complete and there is minor evidence of effort to get assistance on assignment (e.g., office hours attendance or Discord discussions). | Assignment is incomplete and no evidence of seeking assistance. |
| Correctness (30%) | |||
| 8. Test Cases | Percentage of automated test cases that pass. |
Evidence of Persistence:
In the event that you are unable to get your program fully functional, you will receive partial credit based on your evidence showing the amount of effort that went into learning the underlying concepts to complete the assignment or that you persistently sought appropriate assistance. Examples of persistence may include, but is not limited to, the following: Git commit history showing evolution of your program, attendance to office hours (Instructor or TA), asking thoughtful questions in the appropriate Discord forums, formation of study groups, completion of additional practice exercises, reading of third-party resources, etc.
To receive partial credit, you must create a file called
PERSISTENCE.mdin your GitHub repo alongside theREADME.mdfile, and include your evidence of persistence, for example, links to your Discord questions, narrative explaining dates and times of office hour sessions that you attended and what you learned, links to resources that you referenced, links to ChatGPT conversations that you initiated (focusing on concepts not just getting answers), etc. The better you can demonstrate your work on learning, the easier it will be to provide partial credit, so be thorough. Make sure that file is properly committed to your repo, and included in your Gradescope submission.
Submitting Your Work
Your work must be submitted Anchor for degree credit and to Gradescope for grading.
For coding tasks involving Github Classroom:
- Ensure that you
commitandpushyour local code changes to your remote repository. (Note: In general, you should commit and push frequently, so that you have a backup of your work, so that there is evidence that you did your own work, and so that you can return to a previous state easily.) - Upload your submission to Gradescope via the appropriate submission link by selecting the correct GitHub repository from the drop-down list.
- Export a zip archive of your GitHub repository by visiting your repo on GitHub, clicking on the green
Codebutton, and selecting "Download Zip". - Upload the zip file of your repository to Anchor using the form below.
For cases where you answer questions on Gradescope:
- Complete the work in Gradescope by navigating tot he appropriate link.
- Export it as a pdf using th Google Chrome plugin: https://gofullpage.com/. This plugin will do multiple screen captures while scrolling through your document, and then stitch them into a pdf that you can download.
- Upload the generated pdf to Anchor using the form below.
For any work completed outside of GitHub or Gradescope:
- Take either screen captures of your work or export a pdf showing your complete work.
- Submit the materials to Gradescope via the appropriate submission link for the course.
- Upload the screen captures or pdf files to Anchor using the form below.
Note: Anchor submissions can occur at any time during the term, but it is critical that you upload all of your work to Anchor before the last day of the term. Gradescope submissions must be submitted before the deadline (or the late deadline, if applicable).